_base_ = "./fovea_r50_fpn_4x4_1x_coco.py"
model = dict(
    pretrained="torchvision://resnet101",
    backbone=dict(depth=101),
    bbox_head=dict(
        with_deform=True, norm_cfg=dict(type="GN", num_groups=32, requires_grad=True)
    ),
)
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(
        type="Resize",
        img_scale=[(1333, 640), (1333, 800)],
        multiscale_mode="value",
        keep_ratio=True,
    ),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
data = dict(train=dict(pipeline=train_pipeline))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type="EpochBasedRunner", max_epochs=24)
